This issue has been started in this thread DS app running on SD, but not on eMMC (image problem?) - #14 by foreverneilyoung, which finally ended up in the problem to install DS and JP to an eMMC.
I’m having here a procedure, which should be “easy” to follow for you and which illustrates the problem I have:
Required:
- Jetson Nano dev kit 4 GB
- Jetson Nano productive module 16 GB eMMC
Steps to failure:
- Install base system using SDK manager, don’t install any SDK
- Finish initial installation on device
- Clean up and remove some packages to make space for JP and DS:
sudo apt remove chromium*
sudo apt remove firefox*
sudo apt remove thunderbird*
sudo apt remove libreoffice*
sudo apt remove man-db
sudo apt autoremove
-
Install JP manually following How to Install JetPack :: NVIDIA JetPack Documentation 1.3 the low memory variant
-
Do more cleanup
sudo apt remove libnvinfer-samples
sudo apt remove cuda-documentation-10-2
sudo apt remove cuda-samples-10-2
sudo apt remove libvisionworks-samples
sudo apt remove libopencv-samples
sudo apt autoremove
- Install DeepStream SDK
sudo apt install deepstream-5.1 -y
-
You should now have at least 4-5 GB free again, even though it seems, that all required SDKs are installed
-
Open a console, fire up “python3” and enter this sequence
import sys
sys.path.append(‘/opt/nvidia/deepstream/deepstream/lib’)
import gi
gi.require_version(‘Gst’, ‘1.0’)
from gi.repository import GObject, GstGObject.threads_init()
Gst.init(None)
pgie = Gst.ElementFactory.make(‘nvinfer’, ‘primary-inference’)
print(pgie)
None -
You should see “pgie” is None. This is a safe indication, that no DeepStream based Python application will run on this device.
If you do the same on an SD booted system, it will work:
-
Use a 32 GB SD card flashed from the official Nvidia sources instead of the eMMC module
-
Finalize the installation on the device
-
Install DS
sudo apt install deepstream-5.1 -y
- Fire up python an run the same sequenc.
pgie
should now describe a valid object. Python apps will work.